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            <title xml:lang="en">Development of new homogenisation methods for GNSS atmospheric data. Application to the analysis of climate trends and variability.</title>
            <title xml:lang="fr">Développement de nouvelles méthodes d'homogénéisation des données atmosphérique GNSS. Application à l'étude de la variabilité climatique.</title>
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            <idno type="halRefHtml">Statistics [stat]. Sorbonne Universite; IGN (Institut National de l’Information Géographique et Forestière), 2020. English. &lt;a target="_blank" href="https://www.theses.fr/"&gt;&amp;#x27E8;NNT : &amp;#x27E9;&lt;/a&gt;</idno>
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                <title xml:lang="en">Development of new homogenisation methods for GNSS atmospheric data. Application to the analysis of climate trends and variability.</title>
                <title xml:lang="fr">Développement de nouvelles méthodes d'homogénéisation des données atmosphérique GNSS. Application à l'étude de la variabilité climatique.</title>
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                    <forename type="first">Annarosa</forename>
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                <authority type="institution">Sorbonne Universite</authority>
                <authority type="institution">IGN (Institut National de l’Information Géographique et Forestière)</authority>
                <authority type="supervisor">Olivier</authority>
                <authority type="supervisor">Emilie Lebarbier</authority>
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                <term xml:lang="en">atmospheric reanalyse</term>
                <term xml:lang="en">water vapour</term>
                <term xml:lang="en">time series</term>
                <term xml:lang="en">GNSS</term>
                <term xml:lang="en">homogenization</term>
                <term xml:lang="en">segmentation</term>
                <term xml:lang="fr">segmentation</term>
                <term xml:lang="fr">homogénéisation</term>
                <term xml:lang="fr">GNSS</term>
                <term xml:lang="fr">séries temporelles</term>
                <term xml:lang="fr">vapeur d'eau</term>
                <term xml:lang="fr">réanalyses atmosphériques</term>
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              <p>The long series of integrated water vapor content (IWV) measured by GNSS are affected by inhomogeneities linked to changes in the instrumentation, the environment and the data processing procedure. The homogenization of these series is a crucial step for applications in climatology, e.g. estimating the trend and decennial variability. Due to the high natural variability of IWV, segmentation must be applied to IWVdifferences between GNSS observations and a reference which in our application is currently the ERA-Interim reanalysis. We have developed a segmentation method dedicated to the detection of abrupt changes in the mean which takes into account a periodic bias and a heterogeneous variance in these data. The algorithm first calculates the monthly variance with a robust estimator. Then, it estimates the number of breaks fixed iteratively (i) the variable bias and (ii) the positions of the break points and the means of thesignal, this for all the numbers of breaks tested. In a maximum likelihood approach thise stimation is carried out and it is based on the dynamic programming algorithm which is the only one to provide the exact solution in a reasonable time. Finally, the optimal number of change-points is chosen using a penalized model selection method. Four penalty criteria are proposed. The method was tested and optimized using numerical simulations and applied to IWV GNSS data for 120 stations in the global IGS network for the period from January 1995 to December 2010. The results on this data were validated using IGS metadata. The method is implemented and distributed in the form of an R package GNSSseg available on th CRAN.</p>
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              <p>Les series longues de contenu integre en vapeur d’eau (CIVE) mesurees par GNSS sont affectees par des inhomogeneites liees aux changements de l’instrumentation,de l’environnement et de la procedure de traitement des donnees. L’homogeneisation de ces series est une ́etape cruciale pour les applications en climatologie.Du fait de la forte variabilite naturelle du CIVE, la segmentation doit etre appliquee sur des differences de CIVE entre les observations GNSS et une reference qui dans notre application est actuellement la reanalyseERA-Interim. Nous avons developpe une methode de segmentation dediee à la detection de changements abrupts dans la moyenne qui prend en compte un biais periodique et une variance heterogene dans ces donnees. L’algorithme calcule dans un premier temps la variance mensuelle avec un estimateur robuste. Ensuite, il estime à nombre de ruptures fixe de maniere iterative (i) le biais periodique et (ii) les positions des points de rupture et les moyennes du signal, pour tous les nombres de ruptures testes. Cette estimation est realisee au sens du maximum de vraisemblance et s’appuie sur l’algorithme de programmation dynamique qui est le seul à fournir la solution exacte en un temps raisonnable.Finalement, le nombre optimal de ruptures est choisi à l’aide d’une methode de selection de modele penalisee.La methode a ́ete testee et optimisee à l’aide de simulations numeriques et appliquee aux donnees de CIVE GNSS pour 120 stations du reseau IGS. Enfin, les informations de segmentation sont incluses dans un a algorithme de régression linéaire qui est utilisé pour estimer les tendances.La méthode est implémentée dans le package R GNSSseg disponible sur le CRAN.</p>
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